77 lines
2.1 KiB
Markdown
77 lines
2.1 KiB
Markdown
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# PaddleStructure
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安装layoutparser
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```sh
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wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
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pip3 install layoutparser-0.0.0-py3-none-any.whl
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```
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## 1. pipeline介绍
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PaddleStructure 是一个用于复杂板式文字OCR的工具包，流程如下
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在PaddleStructure中，图片会先经由layoutparser进行版面分析，在版面分析中，会对图片里的区域进行分类，根据根据类别进行对于的ocr流程。
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目前layoutparser会输出五个类别:
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1. Text
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2. Title
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3. Figure
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4. List
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5. Table
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1-4类走传统的OCR流程，5走表格的OCR流程。
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## 2. LayoutParser
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[文档](layout/README.md)
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## 3. Table OCR
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[文档](table/README_ch.md)
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## 4. PaddleStructure whl包介绍
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### 4.1 使用
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4.1.1 代码使用
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```python
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import cv2
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from paddlestructure import PaddleStructure,draw_result
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table_engine = PaddleStructure(
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output='./output/table',
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show_log=True)
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img_path = '../doc/table/1.png'
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img = cv2.imread(img_path)
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result = table_engine(img)
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for line in result:
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print(line)
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from PIL import Image
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font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
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image = Image.open(img_path).convert('RGB')
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im_show = draw_result(image, result,font_path=font_path)
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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4.1.2 命令行使用
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```bash
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paddlestructure --image_dir=../doc/table/1.png
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```
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### 参数说明
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大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
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| 字段 | 说明 | 默认值 |
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|------------------------|------------------------------------------------------|------------------|
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| output | excel和识别结果保存的地址 | ./output/table |
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| structure_max_len | structure模型预测时，图像的长边resize尺度 | 488 |
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| structure_model_dir | structure inference 模型地址 | None |
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| structure_char_type | structure 模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.tx |
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